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Nourmohammadi keramatfar
- 1. Proceedings of 10thInternational Conference on Webometrics, Informetrics and Scientometrics & 15th COLLNET Meeting 2014 net 2014 September 3-5, 2014 Technische Universität Ilmenau, Germany Edited by Bernd Markscheffel • Daniel Fischer • Daniela Büttner • Hildrun Kretschmer
- 3. Proceedings of 10thInternational Conference on Webometrics, Informetrics and Scientometrics & 15th COLLNET Meeting 2014 September 3-5, 2014 Technische Universität Ilmenau, Germany Edited by Bernd Markscheffel, Daniel Fischer, Daniela Büttner and Hildrun Kretschmer
- 4. Bernd Markscheffel, DanielFischer, Daniela Büttner and Hildrun Kretschmer Technische Universität Ilmenau Fakultät für Wirtschaftswissenschaften und Medien Institut für Wirtschaftsinformatik P.O. Box 100565 98684 Ilmenau Germany bernd.markscheffel@tu-ilmenau.de daniel.fischer@tu-ilmenau.de daniela.buettner@tu-ilmenau.de kretschmer.h@onlinehome.de Ilmenau, 2014
- 5. 10th International Conferenceon Webometrics, Informetrics and Scientometrics & 15th COLLNET Meeting 2014 v Index Index .......................................................................................................................................... v Invited Papers ........................................................................................................................... 1 Eugene Garfield and Alexander Pudovkin ................................................................................. 3 Journal Impact Factor Reflects Citedness of the Majority of the Journal Papers Liming Liang and Zhen Zhong .................................................................................................. 9 Uncited Papers, Unicited authors and Uncited Topics Weiping Yue ............................................................................................................................ 17 A Scientometric Study on Collaboration between Academia and Industry – Case studies of Chinese leading universities and companies Hildrun Kretschmer and Theo Kretschmer .............................................................................. 21 Three-dimensional Visualization and Animation of Emerging Patterns by the Process of Self-Organization in Collaboration Networks I. K. Ravichandra Rao and K. S. Raghavan ............................................................................. 49 Seven years of COLLNET Journal of Scientometrics and Information Management (2007 -2013) Full Papers .............................................................................................................................. 69 Amir Reza Asnafi and Maryam Pakdaman Naeini .................................................................. 71 A Survey on Collaboration rate of authors in producing Scientific Papers in Quarterly Journal of Information Technology Management during 2009-2014 André Calero Valdez, Anne Kathrin Schaar, Tobias Vaegs, Thomas Thiele, Markus Kowalski, Susanne Aghassi, Ulrich Jansen, Wolfgang Schulz, Guenther Schuh, Sabina Jeschke and Martina Ziefle ........................................................................................... 77 Scientific Cooperation Engineering Making Interdisciplinary Knowledge Available within Research Facilities and to External Stakeholders Arshia Kaul, Sujit Bhattacharya, Shilpa and Praveen Sharma ................................................. 87 Measuring Efficiency of Scientific Research Ashkan Ebadi and Andrea Schiffauerova ................................................................................ 91 How do scientists collaborate? Assessing the impact of influencing factors
- 6. 10th International Conferenceon Webometrics, Informetrics and Scientometrics & 15th COLLNET Meeting 2014 Barbara S. Lancho Barrantes .................................................................................................. 103 Benefits of scientific collaboration Bernd Markscheffel and Johannes Schmidt ........................................................................... 109 A Bibliometric Indicator for the Consideration of Time Related Aspects Following the Example of Twitters Influence Passivity Score Bharvi Dutt and Khaiser Nikam ............................................................................................. 111 International Collaboration in Solar Cell Research in India Carey Ming-Li Chen .............................................................................................................. 121 The Application of Funding Acknowledgment on the Path Analysis of Knowledge Dissemination of Granted Researches Carlos Olmeda-Gómez, María Antonia Ovalle-Perandones, Juan Gorraiz and Christian Gumpenberger ........................................................................................................ 129 Excellence, merit and research team size: a library and information science case study Chen Yue, Zhang Liwei, Wang Zhiqi, Liu Shengbo, Su Lixin and Hou Yu ......................... 139 Influential Bloggers and Active Bloggers on ScienceNet: An Analysis of Popular Blogs Chun Wang, ZhengYin Hu, Miaoling Chai and Hui Wang ................................................... 145 Legal Status Prediction for US Patents on Thermocouples Divya Srivastava, Arvind Singh Kushwah and Mona Gupta ................................................. 153 An Analysis of Collaboration Pattern of Indian S & T Papers (Published during 2005-09) Divya Srivastava, Arvind Singh Kushwah and Mona Gupta ................................................. 163 Impact of Indian S&T Research Papers – Published during 2005-09: through Citation Analysis Divya Srivastava, Sandhya Diwakar and Ramesh Kundra .................................................... 173 Current status of Medical research across the Countries: India, China and Brazil Farideh Osareh and Ismael Mostafavi .................................................................................... 179 Visualizing the co-authorship relations in surgery discipline outputs among Iranian and Global cities Fatemeh Helaliyan Motlagh and Mohammad Hassanzadeh .................................................. 191 Studying the status of knowledge management components in Petrochemical Companies (case study: South Pars Energy Economic Special Zone » Assalouyeh «) Fatemeh Nooshinfar, Aref Riahi and Elham Ahmadi ............................................................ 201 Study of Barriers to Scientific Collaboration of female Scientifics (Case Study of Iranian Women members of University of Tehran) vi
- 7. 10th International Conferenceon Webometrics, Informetrics and Scientometrics & 15th COLLNET Meeting 2014 Gayatri Paul and Swapan Deoghuria ..................................................................................... 209 Indian Journal of Physics: A scientometric analysis Grant Lewison and Richard Sullivan ..................................................................................... 217 Conflicts of Interest Statements on Biomedical Papers Hailong Wang and Minyu Wang ........................................................................................... 227 Core technology fields and innovation cooperation network of electric vehicle industry Hamideh Asadi and Mahsan Poorasadollahi .......................................................................... 237 Structure and Evolution of Library and Information Science in the top Countries of Middle East in terms of Scientific Productions during the years of 1992-2012 Hamzehali Nourmohammadi and Abdalsamad Keramatfar .................................................. 247 The relation between the number of countries’ Rich Files on the web and countries’ economic development Hamzehali Nourmohammadi, Mahdi Keramatfar and Abdalsamad Keramatfar ................... 257 Research in what fields? Determining Iran’s research priorities according to their impact on economic development Handaru Jati ............................................................................................................................ 265 Weight of Webometrics Criteria using Entropy Method Hongfang Shao, Qi Yu and Zhiguang Duan .......................................................................... 269 Detecting the milestones of epigenetics development from 2002 to 2013: a Scientometrics perspective Hou Haiyan, Zhao Nannan, ZhangShanshan, Liang Yongxia and Hu Zhigang .................... 281 Characteristics of the development of NB converging technology Jiang Chunlin, Liu Xue and Zhang Liwei .............................................................................. 293 Data Fetching and Group Characteristics Analysis Based on Sina Microblog Jiang Chunlin, Zhang Liwei and Liu Xue .............................................................................. 301 Survey of the Editorial Board Members for Journals of Library and Information Science in China K. S. Raghavan and I. K. Ravichandra Rao ........................................................................... 309 Mapping Engineering Research in India Leila Nemati-Anaraki and Roya Pournaghi ........................................................................... 317 The Effect of Geographical Proximity on Organizational Knowledge Sharing Li Gu, Weichun Yan and Shule An ........................................................................................ 327 The Relationship between internet attention and market share of operation systems for personal computers vii
- 8. 10th International Conferenceon Webometrics, Informetrics and Scientometrics & 15th COLLNET Meeting 2014 Liu Xiaomin, Sun Yuan and He Jing ..................................................................................... 335 Impact of articles in non-English language journals – A bibliometric analysis of regional journals of China, Japan, France and Germany in Web of Science Lutz Bornmann, Moritz Stefaner, Felix de Moya Anegón and Rüdiger Mutz ...................... 345 Ranking and mappping of universities and research-focused institutions worldwide: The third release of www.excellencemapping.net M.H. Biglu and M. A-Farhangi .............................................................................................. 353 Infometrics analysis of Scientific-literature in Pediatrics obesity Marzieh Yari Zanganeh and Nadjla Hariri ............................................................................. 359 Transactions Reports Analysis Islamic Azad University Marvdasht – branch website: A Case Study Marzieh Yari Zanganeh and Sedigheh Mohammad ............................................................... 367 Use of Six Sigma Concept in University Libraries: A Case Study of Fars province Medical Sciences Library University Masaki Nishizawa and Yuan Sun ........................................................................................... 373 How is scientific research reported in newspapers? – Comparison between press releases and two different national newspapers in Japan Meera and Surendra Kumar Sahu .......................................................................................... 381 Research Output of University College of Medical Science, University of Delhi: A Bibliometric Study Mohammad Hassanzadeh and Babak Akhgar ........................................................................ 395 Relationship between Development Indicators and Contribution to the Science: Experiences from Iran Mursheda Begum and Grant Lewison .................................................................................... 403 European cancer research publications, 2002-13 Nabi Hasan and Mukhtiar Singh ............................................................................................ 413 Library and Information Science Research Output: A study based on Web of Science R. D. Shelton and T. R. Fade ................................................................................................. 427 Which Scientometric Indicators Best Explain National Performance of High-Tech Outputs? Roya Pournaghi and Leila Nemati-Anaraki ........................................................................... 437 The Mutual Role of Scientometrics and Foresight S. L. Sangam, Devika Madalli and Uma Patil ....................................................................... 449 Indicators to Measure Genetics Literature: A Comparative Study of Selected Countries viii
- 9. 10th International Conferenceon Webometrics, Informetrics and Scientometrics & 15th COLLNET Meeting 2014 Sandhya Diwakar and K. K. Singh ........................................................................................ 459 Analysis of the Financial Assistance to Non-ICMR Biomedical Scientists by Indian Council of Medical Research (ICMR) 2009 - 2013 Shantanu Ganguly, P K Bhattacharya and Tanvi Sharma ...................................................... 465 Growth of Literature in Biofuels Research: A Resource Analysis Shilpa, Arshia Kaul and Sujit Bhattacharya ........................................................................... 481 Salient Aspects of India’s Publication activity Soheila Bagheri and Mohaddeseh Dokhtesmati ..................................................................... 485 Comparative study of outputs and scientific cooperation of world's countries in Biomedical engineering field in Science Citation Index in the years 2002-2011 with an emphasis on co-authorship networks Tahereh Dehdarirad, Anna Villarroya and Maite Barrios ...................................................... 497 Women in Science and Higher Education: a bibliometric study Tariq Ashraf ........................................................................................................................... 507 Pattern of Research & Citations: A Study of Three Central Universities Located in Delhi-India Thuraiyappah Pratheepan and W.A. Weerasooriya ............................................................... 529 International research collaboration of Sri Lanka in the last 02 decades (1994 – 2013) based on the SCOPUS database Umut Al and Zehra Taşkın ..................................................................................................... 539 Relationship between Economic Development and Intellectual Production Umut Al, İrem Soydal, Umut Sezen and Orçun Madran ....................................................... 549 The Impact of Turkey in the Library and Information Science Literature Vijayakumar M, Debojyoti Nath and Annapurna SM ........................................................... 559 A study on Indian collaboration among SAARC Countries using Webometrics Methods Wen-Yau Cathy Lin ............................................................................................................... 569 Comparative Study of Journal Impact Factor and Self-Citation Across Asian International Journals Xianwen Wang, Wenli Mao and Chen Liu ............................................................................ 575 Does The Open Access Advantage Exist? An Empirical Study on Citation and Article View Data Xiaoyu Zhu, Zeyuan Liu, Chaomei Chen and Haiyan Hou ................................................... 581 Statistical analysis on interlocking directorate in Chinese listed companies ix
- 10. 10th International Conferenceon Webometrics, Informetrics and Scientometrics & 15th COLLNET Meeting 2014 Yang Zhongkai, Xu Mengzhen and Hanshuang .................................................................... 587 Measurement and Changing Trends of Originality Index Value – In view of NBER Patent Citation Database Yunwei Chen, Yong Deng, Fang Chen, Chenjun Ding, Ying Zheng and Shu Fang ............. 597 A Co-author Based CCS Index Used for Evaluating Scientists’ Performance Zhao Qu, Xiling Shen and Kun Ding ..................................................................................... 609 Comparative Analysis on Technologies between Chinese and American Large-sized Oil Companies based on Patentometrics Posters ................................................................................................................................... 619 List of Accepted Posters ......................................................................................................... 621 x
- 11. 10th International Conferenceon Webometrics, Informetrics and Scientometrics & 15th COLLNET Meeting 2014 1 Invited Papers
- 12. 10th International Conferenceon Webometrics, Informetrics and Scientometrics & 15th COLLNET Meeting 2014 2
- 13. 10th International Conferenceon Webometrics, Informetrics and Scientometrics & 15th COLLNET Meeting 2014 The relation between the number of countries’ Rich Files on the 247 web and countries’ economic development Hamzehali Nourmohammadi* and Abdalsamad Keramatfar** *Shahed University, Tehran, Iran nourmohammadi.h@gmail.com **Scientometrics Section of SID, Tehran, Iran keramatfar@mailfa.com Introduction All the activities related to measuring science started in early 20th century with the works of people like Holm(Braun & others,1985), following Price’s attempts to display the relation between scientific products and countries’ scientific development, using citation indexes for examining countries’ scientific development expanded rapidly. In addition, late in 1960s, Price demonstrated the correlation between countries’ scientific productivity and their GDP and presented the relation between scientific dynamism and economic development (Noroozi Chakoli, 2012). Within the past years this correlation has been confirmed by many researchers like, Vinkler (2008) and Lee & others (2011), which both indicates the significance of evaluation of researches’ findings and verifies its method which is using excessive citation indexes. On the other hand, since the mid-1990s has emerged a new research field, webometrics-“webometrics” itself was coined in 1997 (Almind and Ingwersen 1997), investigating the nature and properties of the Web drawing on modern informetric methodologies (Björneborn & Ingwersen, 2001). the value of webometrics quickly became established through the Web Impact Factor, the key metric for measuring and analyzing website hyperlinks (Thelwall, 2012). Also the need for timely and relevant web-based S&T indicators has become more urgent (Scharnhorst & Wouters, 2006). Nourmohammadi and keramatfar (2013) demonstrated that there exists a correlation between countries scientific production rank and their Rich Files rank on the web and concluded that scientific evaluation of countries could be done based on the number of their Rich Files on the web. According to what was mentioned above, the main problem this study seeks to address is this; is there any relation between countries’ Rich Files on the web and their economic development? Therefore, the questions this study addresses are as follows: What is the number of scientific production of world’s different countries? What is the number of different countries’ Rich Files on the web? What is the amount of GDP indicator of world’s different countries? What is the amount of correlation between countries’ scientific production rank and their GDP rank in comparison with the correlation between countries’ Rich Files rank and their GDP rank? How is the linear relation between the number of countries’ Rich Files and their GDP? Methodology This study is library-based and due to its use of Scientometrics methods lies within scope of Webometrics Researches. Countries’ scientific production data was extracted from SCImago and countries’ GDP data was extracted from World Bank. Countries’ Rich Files data was extracted from Bing search engine in the following way; in order to search, the name of a given country was chosen in the Advance Search section then using the formulae: filetype:pdf,
- 14. 10th International Conferenceon Webometrics, Informetrics and Scientometrics & 15th COLLNET Meeting 2014 filetype:doc, filetype:ppt, the number of Rich Files was determined. Correlation Test was carried out using SPSS19, and Regression Test was carried out using Excell2007. Research Society included all the world countries for which there is the possibility of specific search in Bing search engine. Data was extracted in the second half of August 2013. Theoretical framework Nowadays scientific production is measured based on excessive citation indexes that present bibliographical information of different kinds of scientific productions, because citation index makes identifying and recovering valid information about subject areas possible and provides citation information that relates papers and indicates the degree of validity of papers to a great extent(Noroozi Chakoli, 2012). Using the number of countries’ scientific productions in order to evaluate their scientific development by experts is done by the two large databases ESA and SCImago, the former using Web of Knowledge data and the latter using Scopus data. Along with developments in bibliometrics and emergence of Webometrics some attempts were made to use the web for scientific evaluation. Webometrics is the quantitative analysis of web phenomenon using informetric methods (Noroozi Chakoli, 2102). A useful database in this field is Webometrics (http://webometrics.info) that has been evaluating universities across the world according to their website since 2007. One of the indicators of this database is the number of universities’ Rich Files on the web. Rich Files include PDF, DOC, and PPT; these files have been chosen because the majority of scientific productions are published in one of these formats. Nourmohammadi & Keramatfar (2013) by demonstrating the correlation between the number of countries’ Rich Files on the web and the number of their scientific production proposed that Rich Files can be used for evaluating countries’ scientific development. In this study, the authors examine Nourmohammadi & Keramatfar’s proposal and by examining its correlation with countries’ economic development compare this method with excessive citation indexes method. Findings The findings will be presented in four sections according to the questions put forward in the introduction. 1. What is the number of scientific production of world’s different countries? Table No1 shows the number of world countries’ scientific productions in SCImago. USA, UK, and Japan are ranked first, second, and third. 248 Table 1. The number of countries’ document in SCImago Country Documents Country Documents Country Documents United 6,149,455 Portugal 117,469 Philippines 11,326 States United Kingdom 1,711,878 New Zealand 114,495 Puerto Rico 9,862 Japan 1,604,017 South Africa 107,976 Iceland 9,285 Germany 1,581,429 Argentina 105,216 Latvia 8,396 France 1,141,005 Hungary 100,137 Armenia 8,054 Canada 885,197 Ukraine 98,083 Peru 7,516 Italy 851,692 Ireland 91,125 Oman 6,875 Spain 665,977 Romania 76,361 Georgia 6,381
- 15. 10th International Conferenceon Webometrics, Informetrics and Scientometrics & 15th COLLNET Meeting 2014 249 Country Documents Country Documents Country Documents India 634,472 Egypt 75,610 Azerbaijan 6,135 Australia 592,533 Malaysia 75,530 Costa Rica 5,711 Russian 527,442 Thailand 69,637 Luxembourg 5,121 Federation South Korea 497,681 Chile 58,768 Iraq 4,420 Netherlands 487,784 Slovakia 49,863 Macedonia 4,401 Brazil 391,589 Croatia 49,462 Qatar 4,398 Taiwan 351,610 Pakistan 47,443 Ecuador 3,887 Switzerland 350,253 Saudi Arabia 46,167 Bosnia and Herzegovina 3,524 Sweden 337,135 Slovenia 44,142 Syrian Arab Republic 3,379 Poland 304,003 Tunisia 32,250 Panama 3,043 Turkey 267,902 Colombia 28,817 Bahrain 2,817 Belgium 265,913 Morocco 23,446 Libyan Arab Jamahiriya 2,304 Israel 204,262 Lithuania 21,098 Bolivia 2,298 Austria 188,440 Algeria 21,059 Malta 2,029 Denmark 183,880 Serbia 21,011 Yemen 1,395 Finland 170,476 Jordan 17,126 Guatemala 1,296 Greece 160,760 Estonia 16,573 Albania 1,229 Iran 159,046 Indonesia 16,139 Nicaragua 818 Mexico 144,997 United Arab Emirates 15,698 Paraguay 776 Hong Kong 144,935 Kenya 14,765 El Salvador 768 Czech 142,090 Viet Nam 13,172 Dominican 606 Republic Republic Norway 141,143 Kuwait 12,254 Honduras 595 Singapore 126,881 Lebanon 11,672 2. What is the number of different countries’ Rich Files on the web? Table No2 shows the number of Rich Files for different world countries, with USA, Japan, and Italy having the highest number of Rich Files on the web respectively. Table 2. The number of countries’ Rich Files on the web Country PDF DOC PPT SUM Albania 16100 6720 71 22891 Algeria 46200 5130 1220 52550 Argentina 1190000 158000 25400 1373400 Armenia 13300 3190 1530 18020 Australia 2960000 171000 18800 3149800 Austria 1090000 42800 8560 1141360
- 16. 10th International Conferenceon Webometrics, Informetrics and Scientometrics & 15th COLLNET Meeting 2014 Country PDF DOC PPT SUM Azerbaijan 12000 4490 61 16551 Bahrain 7820 101 44 7965 Belgium 1280000 98900 16600 1395500 Bolivia 64200 7610 1350 73160 Bosnia and Herzegovina 68300 11000 1480 80780 Brazil 4800000 399000 100000 5299000 Canada 4370000 202000 67900 4639900 Chile 639000 67400 22500 728900 Colombia 872000 93800 14100 979900 Costa Rica 127000 24600 14100 165700 Croatia 377000 54200 13700 444900 Czech Republic 708000 101000 22700 831700 Denmark 1070000 74200 11500 1155700 Dominican Republic 42900 2490 734 46124 Ecuador 169000 17200 3970 190170 Egypt 49400 11400 4550 65350 El Salvador 51700 2810 1040 55550 Estonia 161000 23500 8010 192510 Finland 883000 46300 11900 941200 France 5930000 351000 88300 6369300 Georgia 25500 4530 641 30671 Germany 8320000 264000 121000 8705000 Greece 553000 89900 11500 654400 Guatemala 69400 3610 1090 74100 Honduras 24800 1000 90 25890 Hong Kong S.A.R. 704000 60000 21000 785000 Hungary 672000 137000 24500 833500 Iceland 54200 4270 2230 60700 India 1500000 105000 2230 1607230 Indonesia 669000 83400 27600 780000 Iran 536000 93400 19800 649200 Iraq 14800 5460 66 20326 Ireland 528000 47700 8940 584640 Israel 387000 215000 35800 637800 Italy 9660000 978000 120000 10758000 Japan 13100000 444000 39400 13583400 Jordan 24700 9700 4050 38450 Kenya 27700 2490 792 30982 Kuwait 13100 1890 63 15053 250
- 17. 10th International Conferenceon Webometrics, Informetrics and Scientometrics & 15th COLLNET Meeting 2014 251 Country PDF DOC PPT SUM Latvia 114000 51700 3510 169210 Lebanon 23300 3070 1150 27520 Libya 5230 81 31 5342 Lithuania 210000 63600 7910 281510 Luxembourg 78800 3640 766 83206 Macedonia 33100 4660 600 38360 Malaysia 378000 28600 6720 413320 Malta 24800 1690 1860 28350 Mexico 2770000 308000 40400 3118400 Morocco 64600 6720 1570 72890 Netherlands 3570000 252000 36800 3858800 New Zealand 592000 48100 8730 648830 Nicaragua 24800 2060 911 27771 Norway 682000 57600 15000 754600 Oman 7630 2390 47 10067 Pakistan 101000 11400 2100 114500 Panama 60100 4380 1230 65710 Paraguay 23900 2650 1570 28120 Peru 633000 80500 11700 725200 Philippines 77200 4990 1510 83700 Poland 3400000 715000 45800 4160800 Portugal 934000 33900 10200 978100 Puerto Rico 84000 8580 4970 97550 Qatar 12500 1490 61 14051 Romania 737000 152000 18500 907500 Russia 2140000 2150000 147000 4437000 Saudi Arabia 88400 38400 20800 147600 Serbia 187000 20000 6300 213300 Singapore 352000 19000 3960 374960 Slovakia 397000 63800 10400 471200 Slovenia 284000 45200 20800 350000 South Africa 852000 71700 11800 935500 South Korea 686000 30900 67700 784600 Spain 6310000 334000 80500 6724500 Sweden 2540000 148000 21200 2709200 Switzerland 2420000 88300 22100 2530400 Syria 9330 980 45 10355 Taiwan 1320000 603000 127000 2050000 Thailand 1220000 310000 57000 1587000
- 18. 10th International Conferenceon Webometrics, Informetrics and Scientometrics & 15th COLLNET Meeting 2014 Country PDF DOC PPT SUM Tunisia 36100 2350 891 39341 Turkey 1030000 229000 44900 1303900 United Arab Emirates 47700 4830 1520 54050 Ukraine 243000 128000 7490 378490 United Kingdom 6730000 626000 108000 7464000 United States 47500000 3870000 1380000 52750000 Vietnam 141000 135000 4030 280030 Yemen 710 43 3 756 3. What is the amount of GDP indicator of world’s different countries? Table No3 shows countries’ GDP with USA, Japan, and Germany having the highest GDP respectively. 252 Table 3. Countries’ GDP Country GDP Country GDP Albania 13119013351.4499 Lebanon 42945273631.8408 Algeria 207955103846.43 Libya - Argentina 474865096195.534 Lithuania 42245532390.1713 Armenia 9910387657.35811 Luxembourg 57117125224.9936 Australia 1520608083022.1 Macedonia 9663203711.45536 Austria 399649131196.966 Malaysia 303526203366.211 Azerbaijan 67197738734.7695 Malta 8721923076.92308 Bahrain - Mexico 1177271329643.86 Belgium 483709179737.722 Morocco 96729450169.498 Bolivia 27035110167.0902 Netherlands 772226793520.185 Bosnia and 17047582419.997 New Zealand - Herzegovina Brazil 2252664120777.39 Nicaragua 10507356837.651 Canada 1821424139311.45 Norway 499667211001.289 Chile 268313656098.796 Oman - Colombia 369812739540.023 Pakistan 231181921489.54 Costa Rica 45127292711.0687 Panama 36252500000 Croatia 56441607483.0696 Paraguay 25502060502.1181 Czech Republic 195656544502.618 Peru 197110985681.958 Denmark 314242037116.962 Philippines 250265341493.171 Dominican Republic 58951239185.7506 Poland 489795486644.151 Ecuador 84532444000 Portugal 212454101311.391 Egypt 257285845358.245 Puerto Rico 101495811266 El Salvador 23786800000 Qatar - Estonia 21854197100.7971 Romania 169395940257.194 Finland 250024427873.489 Russia 2014774938341.85
- 19. 10th International Conferenceon Webometrics, Informetrics and Scientometrics & 15th COLLNET Meeting 2014 253 Country GDP Country GDP France 2612878387760.35 Saudi Arabia - Georgia 15829300978.6172 Serbia 37488935009.7878 Germany 3399588583183.34 Singapore 274701299733.694 Greece 249098684277.449 Slovakia 91619230769.2308 Guatemala 50806430481.5925 Slovenia 45469230769.5781 Honduras 17967497441.1464 South Africa 384312674445.534 Hong Kong S.A.R. 263259372904.956 South Korea 1129598273324.48 Hungary 125507525410.477 Spain 1349350732836.2 Iceland 13656532879.6765 Sweden 525742140221.402 India 1841717371769.71 Switzerland 632193558707.476 Indonesia 878043028442.369 Syria - Iran - Taiwan - Iraq 210279947255.575 Thailand 365564375701.58 Ireland 210330986079.969 Tunisia 45662043358.0705 Israel - Turkey 789257487307.029 Italy 2013263114238.88 Ukraine 176308825694.203 Japan 5959718262199.13 United Arab Emirates - Jordan 31243324000 United Kingdom 2435173775671.41 Kenya 37229405066.6773 United States 15684800000000 Kuwait - Vietnam 141669099289.418 Latvia 28373857404.0219 Yemen 35645823131.5726 4. What is the amount of correlation between countries’ scientific production rank and their GDP rank in comparison with the correlation between countries’ Rich Files rank and their GDP rank? Tables No.4 and No.5 show the correlation between GDP and the two indicators of countries scientific production rank and countries Rich Files rank. Table 4. Correlation between countries’ scientific production rank and their GDP rank in comparison GDP DOC Correlation Coefficient .879** Sig. (2-tailed) .000 N 80 Correlation is significant at the 0.01 level (2-tailed)
- 20. 10th International Conferenceon Webometrics, Informetrics and Scientometrics & 15th COLLNET Meeting 2014 254 Table 5. Correlation between countries’ Rich Files rank and their GDP rank GDP RICH Correlation Coefficient .897** Sig. (2-tailed) .000 N 80 Correlation is significant at the 0.01 level (2-tailed)E Conclusion Nowadays web and web databases are the first and the most important source for researchers to find information and web richness of every country as its scientific backbone is of highest importance. Moreover, free access to information resources is the context for expanding researches. Existence of scientific resources could be used as a criterion for scientific evaluation (Nourmohammadi & Keramatfar, 2013). The present study sought to investigate the correlation between countries’ Rich Files rank and their economic development rank. The findings indicate that there is a high degree of correlation between the ranking of these two variable. Compared with the correlation between countries’ scientific development Ranking and countries’ economic development ranking (that also has been showed by King.(2004) Price (1978) and Kealey (1996), this correlation does have a higher amount that means this variable has a greater correlation with economic development than science production indicator. The high degree of correlation between this variable and economic development signifies the significance of web as the context of research and free access to information resources. Moreover this correlation demonstrates that this variable can be used along with other indicators to evaluate countries’ scientific development. Another point worth noticing is the fact that having access to web, disregarding the initial expenses, is free and evaluation according to this can be easily done, while having access to databases like Web of Knowledge and Scopus involves expenditure; however, it should be taken into account that due to the dynamic nature of web and its constant and rapid changes, Webometric results have always been tentative. Other researches following this study can be concerned with the evaluation of the nature of these files and their types – article, manual, handbook, book, etc.; meanwhile conducting causality test between these two variables can result in helpful findings. References Braun, T, Glanzel, W, Schubert.(1985). SIENTOMETRICS INDICATORS: A 32-country Comparative Evaluation of Publishing Performance and Citation Impact. World Scientific Publishing Co. Björneborn, L., & Ingwersen, P. (2001). Perspective of webometrics. Scientometrics, 50(1), 65-82. Almind, T. C., & Ingwersen, P. (1997). Informetric analyses on the world wide web: methodological approaches to ‘webometrics’. Journal of documentation, 53(4), 404-426. Wouters P, Scharnhorst A. Web indicators: a new generation of S&T indicators? Cybermetrics 2006; 10. Available at http://www. cindoc.csic.es/cybermetrics/articles/v10i1p6.html. Vinkler, p. (2008). “Correlation between the structure of scientific research, scientometric indicators and GDP in EU and non-EU countries”. Scientometrics. 74(2). pp. 237-254. Lee, Ling-chu. Lin, Pin-hua. Chung, Yun-wen. Lee, Yi-yang. (2011). “Research output and economic output: a Granger causality test”. Scientometrics, 89(2). pp 465-478. Noroozi Chakoli, Abdolreza (2012). Introduction to Scientometrics. Samt. Thelwall, M. (2012). A history of webometrics. Bulletin of the American Society for Information Science and Technology, 38(6), 18-23.
- 21. 10th International Conferenceon Webometrics, Informetrics and Scientometrics & 15th COLLNET Meeting 2014 Nourmohammadi H, Keramatfar A. 2013. Assessment of scientific presence of Estonia in web; a new Approach. In: Proceeding of WIS 2013, Estonia, 9th International Conference on Webometrics, Informetrics and Scienctometrics & 14th COLLNET Meeting. 15- 17 August. Price, DJ, 1967. Nations can publish or perish. Science and Technology. 70: 84-90. Thelwall M, 2012. A history of webometrics. Bulletin of the American Society for Information Science and Technology, 38(6): 18-23. Vinkler P. 2008. Correlation between the structure of scientific research, scientometric indicators and GDP in EU and non-EU countries. Scientometrics, 74(2): 237-254. Wouters P, Scharnhorst A. 2006. "Web indicators: a new generation of S&T indicators?." Cybermetrics: International Journal of Scientometrics, Informetrics and Bibliometrics (10):7. 255
- 22. 10th International Conferenceon Webometrics, Informetrics and Scientometrics & 15th COLLNET Meeting 2014 256
- 23. 10th International Conferenceon Webometrics, Informetrics and Scientometrics & 15th COLLNET Meeting 2014 Research in what fields? Determining Iran’s research priorities 257 according to their impact on economic development Hamzehali Nourmohammadi*, Mahdi Keramatfar** and Abdalsamad Keramatfar*** *Shahed University, Tehran, Iran nourmohammadi.h@gmail.com **Tarbiyat Modaress University, Tehran, Iran mkeramatfar@gmail.com ***Scientometrics Section of SID, Tehran, Iran keramatfar@mailfa.com Introduction The ability to assess a country’s scientific situation is of pressing importance. Since all the sciences do not have the same degree of application (Berer, 2012) and in a particular time an economy can develop technology in a number of sections and it is difficult to predict which technologies would more beneficial (Kealey, 1996), determining research priorities is a very important issue for science and technology policy-makers (Lee et al, 2011). One of the Iran’s attempts is the Country’s Comprehensive Scientific Plan document that in the third season determines the country’s scientific and technological priorities. On the other hand, economic issues have to be deal with effectively in making any decision related to science and technology (Salter 2001). It is also of highest importance to decide which fields are economically worth investing. Ray and Lal (2000) suggest that developed countries should investment in basic research and developing countries should invest in education, infrastructures, and engineering because these fields have the biggest impact on economic development. Vinkler (2008) holds out the effect of development level on researches’ outputs and argues that the relation between economic development and researches’ outputs differs in different countries; in developed countries there is no significant relation between economic development and researches’s outputs while in central and Eastern European countries there is more significant relation; he argues that developed countries are more capable of supporting basic researches, therefore, their researches includes basic researches and deals less with future researches. Chuang et al. (2010) indicated that the research areas in which Singapore, Taiwan, and South Korea have been working during the last decade have been engineering areas. Newly industrializing countries, especially South Korea and Taiwan, have been focusing on understanding and spreading the existing technology rather than producing new technology. Moreover, Japan’s policy of science and technology is increasingly concerned with technologies with economic importance. Kealey (1996) argues that concentration on basic science is not effective in advancing technology. Since Iran is a developing country, and due to the presence of oil resources, research expenses may be directed toward unimportant areas that have the least impact on economic development. Thus, the present paper aims to determine which research area will have the most central effects on the country’s economic development. Research purposes The main objective of this study is to determine of Iran’s research priorities according to their impact on economic development.
- 24. 10th International Conferenceon Webometrics, Informetrics and Scientometrics & 15th COLLNET Meeting 2014 Other objects of this research include: Quantity in science productions in countries’ subject areas Quantity of GDP during different years Determining of relation between the country’s different subject areas of science 258 production and GDP The majors of the greatest impact on GDP in engineering field Theatrical Framework Today assessment of scientific papers is performed based on Citation Indexes that collect bibliography information because these Indexes provide Ability to identify and recover valid information of various subject areas and citation information that link the work to other works, and To a large extent reflects the impact of the paper(Noroozi Chakoli, 2011). The most important of these indexes are Web of Knowledge and Scopus. In 2007 Scimago Research Group offered a tool based on Scopus data that provide ability to study and comparison of scientific production in two main Unit, countries and journals. This tool divides all scientific papers to 320 disciplines and 27 areas that provide ability to subjective analysis. There is a broad literature in studying the relation between science and technology. Price(1967) stated that academic researches Create a generation of researches and future researches of these researchers and will cause economic prosperity also basic researches that usually performs by universities are input of R&D activities. Jaffe(1989) showed that academic researches improve industrial R&D. in fact providing basic research spending by government, many industrials do not pay for basic research in development of technology and they will be able to use it, thus social benefits will result. Diamond(1996) stated that science is Leader of Technology and technology will lead to productivity and growth. Narin et al(1997) studied citation in patents to scientific papers and showed that this type of citation grew and concluded that Technology is based on science. Mansfield et al(1991) studied new goods and process and stated that 11% of new product and 9% of new process could not be improved without academic research. Martin et al (1996) stated the various types of contributions that publicly funded research makes to economic growth: 1. Increasing the stock of useful knowledge; 2. Training skilled graduates; 3. Creating new scientific instrumentation and methodologies; 4. Forming networks and stimulating social inter- action; 5. Increasing the capacity for scientific and technological problem-solving; 6. Creating new firms. On other hand, some of R&D researches publish a paper of their work in scientific journals, so assessment of papers can obvious economic activities in R&D sectors. Overall Evidences show that publicly funded basic research have many benefits (Salter&Martin, 2001). One of the common tests in econometrics is Granger causality test. In The Granger causality test for testing the hypothesis; "(X_t) is not Granger cause of (Y_t)" a (VAR) model is formed: ୩ Y୲ ൌ α୧ ୧ୀଵ ୩ Y୲ି୧ β୧ ୧ୀଵ X୲ି୧ u୲ So this linear model is estimated and the significant assumption is tested. If the assumption coefficients of X୲ି୧ i.e. β୧ being zero Confirm then X୲ is not Granger cause of Y୲. In fact if the being zero assumption of test is rejected X୲ is cause of Y୲. Since there is a time gap between
- 25. 10th International Conferenceon Webometrics, Informetrics and Scientometrics & 15th COLLNET Meeting 2014 publication and their impact (King, 2004), here we test the impact of science on welfare with a lag. Methodology This study is applied and descriptive - based and due to its use of Scientometric methods. The data related to the country’s scientific production were extracted from Scimago data base, Country Search section. Data related to GDP were extracted from the World Bank’s data base. In order to analyze the data Eviews7 was employed and stationary and Granger test were administered. The data were gathered early in December 2013. Findings First Data is an indication of the country’s science production from 1996 to 2012 in Scimago data base. As is seen, medical science has the highest share, engineering and chemistry rank second and third. 259 Table1. Number of scientific production of Iran in different subjects 1996-2011 Subject Area 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 Agricultural and Biological Sciences 78 84 83 119 129 137 227 274 368 513 1134 1597 1846 2088 2574 3686 Arts and Humanities 4 4 2 2 1 7 2 4 4 17 25 32 46 75 76 127 Biochemistry, Genetics and Molecular Biology 70 80 82 115 131 182 244 337 449 558 816 1256 1474 1666 2009 2824 Business, Management and Accounting 4 2 6 4 6 1 4 8 13 17 26 33 77 108 153 217 Chemical Engineering 51 74 72 86 114 135 181 229 320 454 612 792 939 1142 1457 1987 Chemistry 142 168 236 316 363 502 616 838 1 1271 1515 1931 2155 2622 3016 3605 Computer 40 53 54 56 79 90 115 219 277 412 518 648 101 1117 139 1956 Science Decision Sciences 12 14 15 8 17 11 17 16 29 57 72 93 146 212 238 276 Dentistry 2 - 1 3 9 5 9 19 22 22 32 63 83 116 117 137 Earth and Planetary Sciences 28 45 35 36 67 64 85 148 161 204 263 334 337 537 601 807 Economics, Econometrics and Finance 2 - 2 1 2 2 2 4 2 4 10 9 16 32 69 150 Energy 16 27 22 22 17 24 60 75 97 102 167 208 325 404 580 873 Engineering 133 163 161 176 245 331 464 766 1028 1106 1471 1687 2125 3554 4293 5761 Environmental 26 33 33 41 45 62 109 136 197 245 347 583 693 1031 1281 2131 Science Health Professions 1 - 1 3 1 7 5 12 35 41 52 58 63 62 82 107
- 26. 10th International Conferenceon Webometrics, Informetrics and Scientometrics & 15th COLLNET Meeting 2014 Subject Area 260 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 Immunology and Microbiology 19 22 23 33 41 50 72 114 117 171 263 325 412 446 688 898 Materials Science 63 86 88 109 144 208 277 405 528 711 937 1103 1619 2061 2599 3412 Mathematics 47 71 78 107 111 137 186 247 407 559 697 900 952 1299 157 2206 Medicine 124 188 165 161 194 276 480 720 827 1545 2346 305 3818 4499 5359 6684 Multi-disciplinary 11 12 14 23 22 13 33 30 62 48 138 216 511 620 522 1665 Neuroscience 10 10 9 15 17 22 31 45 56 67 107 156 176 196 218 309 Nursing - - 1 3 4 1 5 3 12 24 33 58 108 108 96 146 Pharmacology, Toxicology and Pharmaceutics 31 48 66 73 72 58 110 117 198 237 332 419 440 647 775 1169 Physics and Astronomy 64 77 109 115 133 148 234 283 420 472 809 103 1357 1675 1939 2577 Psychology - 2 2 7 10 8 12 23 19 21 31 42 45 48 307 820 Social Sciences 10 5 8 8 11 9 29 48 48 75 106 150 190 306 653 1761 Veterinary 28 22 27 25 30 21 31 52 64 87 143 151 337 310 378 512 Second Data set shows the Iran’s GDP from 1996 to 2011. Table2. GDP per capita of Iran 1996-2011 year GDP per capita (current US$) year GDP per capita (current US$) year GDP per capita (current US$) year GDP per capita (current US$) 1996 1799.672 2004 2353.931 2000 1536.715 2008 4899.312 1997 1683.634 2005 2737.112 2001 1726.63 2009 4931.283 1998 1611.308 2006 3140.198 2002 1718.965 2010 5674.924 1999 1613.599 2007 3983.582 2003 1975.539 2011 6815.57 Third Data includes the results of Granger’s causal test for the country’s different subject areas of science production, yellow cells indicate significance at the level of 0.05 and green cells indicate significance at the level of 0.01. As is observed, nursing has had the greatest impact on GDP, and at the same time, nursing has been influenced most by GDP. Table3. Causality test in between different subject areas and GDP causality Causality direct Science production to GDP GDP to Science production Agricultural and Biological Sciences 0.4669 0.2276 Arts and Humanities 0.0163 0.0304 Biochemistry, Genetics and Molecular Biology 0.0673 0.0327 Business, Management and Accounting 0.0064 0.1396
- 27. 10th International Conferenceon Webometrics, Informetrics and Scientometrics & 15th COLLNET Meeting 2014 261 causality Causality direct Science production to GDP GDP to Science production Chemical Engineering 0.0704 0.2858 Chemistry 0.0253 0.1613 Computer Science 0.7136 0.212 Decision Sciences 0.0513 0.0112 Dentistry 0.0499 0.0822 Earth and Planetary Sciences 0.0166 0.6185 Economics, Econometrics and 0.016 0.1455 Finance Energy 0.0564 0.0181 Engineering 0.0024 0.4192 Environmental Science 0.0784 0.0134 Health Professions 0.9895 0.0412 Immunology and Microbiology 0.1873 0.4948 Materials Science 0.0283 0.2405 Mathematics 0.3154 0.0369 Medicine 0.2462 0.0697 Multidisciplinary 0.0052 0.0098 Neuroscience 0.2163 0.0198 Nursing 0.0002 0.0029 Pharmacology, Toxicology and 0.0284 0.5019 Pharmaceutics Physics and Astronomy 0.1291 0.0168 Psychology 0.1354 0.2268 Social Sciences 0.0042 0.0292 Veterinary 0.0223 0.0182 As was mentioned before, each of the 27 separated areas in Scimago includes different majors, in engineering field such a separation has been carried out. Table 4 indicates the result of causal test for different engineering majors. Table 4 shows that eco-medicine engineering, civil engineering, system and supervising engineering, industry and production engineering at the level of 0.01, and mechanical engineering, material mechanics, and science of material at the level of 0.05 have impact on GDP. Table4. Causality test for different engineering areas Subject Area Impact on GDP Impact of GDP Aerospace Engineering 0.41 0.16 Architecture 0.32 0.001 Automotive Engineering 0.8 0.08 Bioengineering 0.0009 0.13 Construction 0.59 0.22 Civil and Structural 0.003 0.17 Engineering
- 28. 10th International Conferenceon Webometrics, Informetrics and Scientometrics & 15th COLLNET Meeting 2014 262 Computational Mechanics 0.06 0.86 Control and Systems Engineering 0.008 0.12 Electrical and Electronic Engineering 0.25 0.51 Engineering (miscellaneous) 0.38 0.42 Industrial and Manufacturing Engineering 0.008 0.04 Mechanical Engineering 0.02 0.006 Mechanical Engineering 0.04 0.1 Media Technology 0.92 0.42 Ocean Engineering 0.99 0.0041 Safety, Risk, Reliability 0.29 0.007 and Quality Chemical Engineering 0.0704 0.2858 Computer Science 0.7136 0.212 Material science 0.0283 0.2405 Conclusion The major's eco-medicine engineering, civil engineering, system and supervising engineering, industry and production engineering at the level of 0.01 and the major's mechanical engineering, material mechanics, and science of material at the level of 0.05 have impact on GDP. In other words, these majors should have research priority in Iran. Of course, it should be mentioned that since industry and production engineering and mechanical engineering are affected by GDP, it might mean that these sections have been financed. Being affected by GDP presented above could be analyzed in this way: if an increase in GDP has had effects on a group or a major, it probably means that GDP increase has been accompanied by budget increase in that group or major, therefore, if the reverse relation, i.e. the effectiveness of that group or major in GDP is not significant, continuing to increase the budget for that group or major cannot be justified. Consequently, in engineering group majors like architecture engineering and safety engineering do involve the risk and problem just mentioned and therefore investing in these sectors is not justifiable. References Vinkler, P. (2008). “Correlation between the structure of scientific research, scientometric indicators and GDP in EU and non-EU countries”. Scientometrics. 74(2). pp. 237-254. Narin, F., Hamilton, K., Olivastro, D., 1997. The linkages between US technology and public science. Research Policy 26, 317–330. Lee, Ling-chu. Lin, Pin-hua. Chung, Yun-wen. Lee, Yi-yang. (2011). “Research output and economic output: a Granger causality test”. Scientometrics, 89(2). pp 465-478. Borer, Kealey. (2012). The state is an enemy of science: a review of terence kealey’s the economic laws of scientific research. Libertarian papers, 4(2). Pp 89-96. Terence Kealey. The Economic Laws of Scientific Research. London: Macmillan, 1996.
- 29. 10th International Conferenceon Webometrics, Informetrics and Scientometrics & 15th COLLNET Meeting 2014 SALTER, A. J., B. R. MARTIN, The economic benefits of publicly funded basic research: A critical review, Research Policy, 30 (3) (2001) 509–532. Godin, B. and Doré, C. (2004) ‘Measuring the Impacts of Science: Beyond the Economic Dimension,’ CSIIC Working Paper. Rai, L. P., & Lal, K. (2000). Indicators of the information revolution. Technology in Society, 22, 221– 235. Chuang, Y. W., Lee, L. C., Hung, W. C., & Lin, P. H. (2010). Forgoing into the innovation lead—A comparative analysis of scientific capacity. International Journal of Innovation Management, 14(3), 511–529. Diamond Jr, A.M. (1996), ‘The economics of science’, Special Issue of The International Journal of Knowledge Transfer and Utilization, 9, 3–49. Jaffe, A., 1989. Real effects of academic research. American Economic Review 79, 957–970. Price, Derek J.De Solla., 1967, “Nations can publish or perish”, Science and Technology. 70. pp.84- 90. Mansfield, E. et al., 1991. Academic research and industrial innovation. Research Policy 20, 1–12. Martin, B., Salter, A., Hicks, D., Pavitt, K., Senker, J., Sharp, M., Von Tunzelmann, N., 1996. The Relationship Between Publicly Funded Basic Research and Economic Performance: A SPRU Review. HM Treasury, London. Noroozi Chakoli, Abdoreza. 2011. Introduction to Scientometrics (Principles, concepts, relations and roots). Tehran: SAMT. 263